3 citations · 3 across the 3 of their papers we have counts for
6 papers
Active noise cancellation extends the benefit of AI speech enhancement in real hearing-aid recordings
Andrew T. Sabin, Linkai Li
Active noise cancellation (ANC) is common in headphones but rare in hearing aids, and its value for understanding speech in noise is not established. Algorithms that improve signal…
Reference-aware SFM layers for intrusive intelligibility prediction
Hanlin Yu, Haoshuai Zhou, Boxuan Cao +3
Intrusive speech-intelligibility predictors that exploit explicit reference signals are now widespread, yet they have not consistently surpassed non-intrusive systems. We argue tha…
Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Boxuan Cao, Linkai Li, Hanlin Yu +3
Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive meth…
No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
Haoshuai Zhou, Changgeng Mo, Boxuan Cao +2
Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a…
Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People
Haoshuai Zhou, Boxuan Cao, Changgeng Mo +2
Speech foundation models (SFMs) have demonstrated strong performance across a variety of downstream tasks, including speech intelligibility prediction for hearing-impaired people (…
Interpreting Audiograms with Multi-stage Neural Networks
Shufan Li, Congxi Lu, Linkai Li +3
Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies. They are used by audiologists to diagnose hearing loss, and further…